
The FR-IQA model tries to simulate the HVS characteristics with good performance measures [1]}, [2]}, [3]}, [4]}. Two main reasons for the success of FR-IQA can be attributed to the deep learning based perceptual properties of the reference image and the hand-crafted features derived from statistical metrics which are similar to the HVS. Hence, it becomes easier to build a system that minimises the difference between these two corresponding features. In order to effectively model the properties of the HVS, a couple of related systems have been proposed. Zhang et al. [5]} proposed a similarity index metric which calculated the phase congruence and gradient magnitude to represent the HVS system, while [6]} implemented an efficient standard deviation pooling strategy which demonstrated that the gradient magnitude of an image still holds true as a technique for representing the HVS. [7]} adopted a novel deviation pooling technique to compute the quality score from the gradient and chromaticity similarities as a measure for local structural distortions.
